Page 50 - DCAP603_DATAWARE_HOUSING_AND_DATAMINING

Data Warehousing and Data Mining Sartaj Singh, Lovely Professional University

notes unit 3: Data Mining techniques

contents

Objectives
Introduction
3.1 Statistical Perspective on Data Mining
3.2 What is Statistics and why is Statistics needed?
3.3 Similarity Measures
3.3.1 Introduction

3.3.2 Motivation
3.3.3 Classic Similarity Measures
3.3.4 Dice
3.3.5 Overlap

3.4 Decision Trees
3.5 Neural Networks
3.6 Genetic Algorithms
3.7 Application of Genetic Algorithms in Data Mining
3.8 Summary

3.9 Keywords
3.10 Self Assessment
3.11 Review Questions
3.12 Further Readings

objectives

After studying this unit, you will be able to:
l z Know data mining techniques
l z Describe statistical perspectives on data mining

l z Explain decision trees
introduction

Data mining, the extraction of hidden predictive information from large databases, is a powerful
new technology with great potential to help companies focus on the most important information
in their data warehouses. Data mining tools predict future trends and behaviors, allowing
businesses to make proactive, knowledge-driven decisions. The automated, prospective analyses
offered by data mining move beyond the analyses of past events provided by retrospective
tools typical of decision support systems. Data mining tools can answer business questions that
traditionally were too time consuming to resolve. They scour databases for hidden patterns,
finding predictive information that experts may miss because it lies outside their expectations.

44 LoveLy professionaL university